Making L&D Matter: Turning Learning Outcomes into Business Impact

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A few months into almost every new L&D leadership role I’ve taken on, a familiar question starts to emerge: how do we demonstrate the value of learning to the business? It’s a reasonable question, particularly when organisations are investing significant resources in developing their people and leaders want to understand what they’re getting in return. Yet, despite the amount of data L&D teams collect, we often struggle to provide a convincing answer.

Most L&D teams can tell you how many employees attended a programme, how many completed their learning, what they scored in an assessment and whether they enjoyed the experience. We produce dashboards, reports and presentations that demonstrate the volume of learning activity taking place across the organisation. While this information is useful, it tells us very little about whether employees are performing differently or whether the business is achieving better results as a consequence of our interventions.

I believe this is one of the most important shifts L&D teams need to make. We need to move beyond demonstrating that learning happened and start demonstrating how learning contributes to the outcomes our organisations are trying to achieve. After all, leaders don’t invest in learning for the sake of learning. They invest because they expect it to help the organisation achieve something.

Are we measuring activity or impact?

When I think about learning measurement, I distinguish between two broad categories of metrics: participation metrics and business impact metrics. Both have a place in understanding the effectiveness of our work, but they answer fundamentally different questions.

Participation metrics tell us whether learning happened. They include attendance, completion rates, assessment results and learner satisfaction. These measures help us understand whether employees are accessing learning, progressing through programmes and developing the intended knowledge or skills. They’re important indicators of how effectively we’re delivering learning, but they don’t necessarily tell us whether anything has improved in the business.

Business impact metrics, on the other hand, help us understand whether learning has contributed to an improvement in organisational performance. Depending on the capability we’re developing, these might include sales, productivity, customer satisfaction, time to competence or error rates. These are the measures that connect our work to the outcomes the business is trying to achieve.

Consider a sales training programme. We might report that 95% of employees completed the programme and gave it an average satisfaction rating of 4.8 out of 5. Those results suggest that the programme reached its intended audience and was positively received, but they don’t tell us whether employees are now more effective at selling. To understand that, we’d need to examine whether conversion rates are improving, whether employees are applying the sales techniques they’ve learned and whether customers are having better experiences.

This distinction matters because a programme can perform exceptionally well against participation metrics without making a meaningful difference to business performance. Equally, a programme with modest satisfaction scores might still contribute to significant improvements in how employees work. If we rely too heavily on participation data, we risk confusing successful delivery with successful outcomes.

I often summarise the distinction by saying that participation tells us we delivered, while business impact tells us we mattered. That doesn’t mean participation measures should be abandoned, but rather that they should form part of a broader approach to understanding the value learning creates.

Why measurement needs to begin before learning design

One of the biggest challenges with measuring learning impact is that we often think about measurement too late. A stakeholder approaches the L&D team with a request, and we begin discussing the audience, content, delivery method and timeline. We design the programme, launch it and only then start considering how we’ll evaluate its success. By that point, we’ve missed an important opportunity to establish what the intervention was intended to achieve and how we would recognise meaningful improvement.

I’ve found that two principles can fundamentally change these conversations. The first is to establish how we’ll know whether the intervention has made a difference, and the second is to connect that improvement to something the business already measures.

Start by understanding what needs to improve

Imagine a business leader approaches your team requesting refresher training. It’s a familiar request and one that L&D can respond to relatively easily. We could identify the relevant content, create a short programme, invite employees to attend and report on completion. However, before agreeing to the intervention, I’d want to understand what prompted the request in the first place.

Perhaps employees are making too many errors in a particular process, customer complaints have increased or productivity has declined. Each of these situations represents a different business problem, and understanding the problem allows us to investigate what might be causing it. Employees may lack the knowledge or skills required to perform effectively, but they may also be struggling with complicated processes, unreliable systems or unclear expectations. In those circumstances, providing additional training may have very little effect on the outcome we’re trying to improve.

By exploring the business problem before designing a solution, we can establish whether learning is the appropriate response and identify the specific capability that needs to be developed. We can also agree upfront what improvement we’d expect to see if the intervention is successful. The conversation moves away from simply delivering the training that was requested and towards understanding how L&D can contribute to solving a business problem.

Connect learning to what the business already measures

The second principle is that L&D teams shouldn’t always need to invent new metrics to demonstrate their value. Most organisations already measure the outcomes that matter to them. Sales teams monitor performance, operations teams track productivity, customer-facing teams measure service quality, and managers have performance indicators they’re accountable for delivering. Our opportunity is to understand these measures and identify where learning can make a meaningful contribution.

Consider an organisation introducing AI into its design processes. L&D could develop training on the new tools, track attendance, measure employee confidence and collect feedback on the learning experience. However, these measures wouldn’t tell us whether the investment in AI was delivering the intended business value. To understand that, we’d first need to establish why the organisation was introducing AI and what it hoped to achieve.

Perhaps the objective is to reduce the time required to complete certain design tasks, improve productivity or enable teams to explore more creative options. Understanding these objectives changes how we approach the learning intervention. We can identify the capabilities employees need to use AI effectively, design opportunities for them to apply those capabilities and agree on the existing business measures that could demonstrate progress.

This also changes the role L&D plays in the organisation. Instead of simply helping employees learn how to use a new tool, we’re contributing to the organisation’s ability to realise value from its investment. That requires us to understand the business context, work closely with stakeholders and take an interest in what happens after the formal learning experience ends.

A practical framework for connecting learning to business impact

I believe measurement should be integrated into the entire learning process rather than treated as an evaluation exercise at the end. I’ve developed a six-step framework that helps me approach learning interventions with a clearer connection between the business problem, the capabilities we’re developing and the outcomes we’re trying to achieve.

1. Identify the business problem

The starting point is understanding what the business is trying to solve. This involves exploring what isn’t working as expected, why the problem matters, who is affected and what the consequences are if nothing changes. It also requires us to challenge the assumption that learning is necessarily the answer. When we understand the business problem, we’re better positioned to determine whether a capability gap exists and whether L&D has a meaningful role to play in addressing it.

2. Agree the business metric

Once the problem is clear, the next step is to agree how we’ll know whether we’ve made a difference. Ideally, this involves identifying an existing business measure, establishing current performance and agreeing what improvement we’d hope to see. The metric should be relevant to the problem and sufficiently sensitive to help us understand whether performance is changing over time.

It’s also important to recognise that business outcomes are rarely influenced by learning alone. Changes in processes, leadership, systems, market conditions and employee motivation can all affect performance. Rather than claiming responsibility for every improvement in a business metric, L&D should aim to demonstrate a credible contribution by connecting the capabilities developed through learning to observable changes in behaviour and performance.

3. Identify the capability required

With the business outcome established, we can work backwards to understand what employees need to know, do or believe differently to achieve it. This might involve developing new knowledge, strengthening practical skills or changing established behaviours. It also helps us distinguish between problems that can be addressed through learning and those that require other forms of support or organisational change.

The more clearly we define the required capability, the more focused our intervention can become. Instead of creating a broad programme around a topic, we can concentrate on the specific knowledge, skills and behaviours that will help employees perform differently in their roles.

4. Design the learning solution

Only once we’ve established the business problem, desired outcome and required capability should we determine the most appropriate learning intervention. Depending on the need, this might involve formal training, coaching, digital learning, practical exercises, on-the-job support or a combination of approaches.

The design should also consider how employees will transfer what they’ve learned into their everyday work. Opportunities for practice, manager support and reinforcement are often as important as the formal learning experience itself. If we want to influence business performance, we need to design for application rather than simply the acquisition of knowledge.

5. Measure throughout the journey

Meaningful measurement requires us to collect evidence before, during and after the intervention. Before learning begins, we need to understand current performance and establish a baseline against which improvement can be assessed. During the intervention, participation, assessment and feedback data help us understand whether the learning experience is working as intended.

After the intervention, our attention should shift towards application and performance. Are employees using what they’ve learned? Are the intended behaviours changing? Is there evidence of improvement in the business metrics we agreed upfront? The timing of these measures matters because some outcomes may become visible relatively quickly, while others will take months to emerge. Looking at the full journey helps us understand not only whether the intervention was effective, but also where improvements may be needed.

6. Use the results

The final step is to use the evidence we’ve collected to make better decisions. If an intervention is producing the intended results, we can identify what contributed to its success and consider whether the approach should be expanded or adapted for other audiences. If the results are disappointing, we can investigate whether the learning was ineffective, the capability gap was incorrectly diagnosed or employees faced barriers to applying what they’d learned.

This is where measurement becomes particularly valuable. The purpose isn’t simply to produce a report that demonstrates what L&D has delivered, but to generate insights that improve future interventions and inform decisions about where the organisation should invest its resources.

What changes when L&D starts measuring business impact?

I believe that taking this approach changes far more than the way L&D reports its results. It fundamentally changes the conversations we have with stakeholders, the interventions we design and the decisions we make about investment.

When we begin with the business problem, our conversations become more strategic. Rather than discussing how many training sessions we can deliver or which learning format to use, we begin exploring the performance challenges the organisation is facing and the capabilities required to address them. This positions L&D as a partner in improving business performance rather than simply a provider of learning services.

It also improves the quality of our learning design. When we’re clear about what success looks like, we can select interventions that are more closely aligned with the outcomes we’re trying to achieve. We can build opportunities for application into the learning experience and identify the support employees will need to translate their new capabilities into improved performance.

Perhaps most importantly, this approach enables better investment decisions. When we understand which interventions are contributing to meaningful improvements, we can direct resources towards the initiatives that matter most. We can identify approaches worth expanding, recognise those that need improvement and make more informed decisions about where learning investment is likely to create the greatest value.

These changes also strengthen L&D’s credibility. When we understand the business, speak the language of performance and use evidence to demonstrate our contribution, we become more effective partners to the leaders we support. Measurement helps us build buy-in, secure investment and demonstrate that developing people is an important part of achieving organisational objectives.

Making the shift

If you’re looking to strengthen how your team measures learning, I would encourage you to begin with one current initiative rather than attempting to redesign your entire measurement approach. Choose a programme you’re developing or have recently launched, and have a conversation with the business stakeholder about what they’re hoping will improve as a result of the learning. Identify a relevant business metric, consider what employees need to do differently and agree how you’ll gather evidence of that change.

You don’t need a sophisticated measurement system to begin making progress. What matters is having clarity about the problem you’re trying to solve, agreement on what success looks like and a commitment to use the evidence you collect to inform future decisions. As this becomes part of your team’s approach to learning design, measuring impact becomes a natural extension of the work rather than an additional exercise undertaken after delivery.

For me, the most important shift is moving away from asking whether we’ve successfully delivered a learning programme and towards asking whether we’ve helped the business achieve something meaningful. When we make that shift, we begin to demonstrate the contribution L&D makes to organisational performance and create a stronger case for continued investment in developing people. When we measure what matters, learning becomes an investment, not a cost.

About the author

I’m Sheree Comninellis. I work in corporate Learning & Development, with a focus on building enterprise capability. I write and speak about learning, leadership and change.

Have a perspective to share or want to continue the conversation? Connect with me on LinkedIn or get in touch through my website.

You Launched It. So Why Aren’t They Using It?

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You’ve spent months preparing for the launch of a new technology. The business case was approved, the system was selected and configured, and employees received the necessary training and communication. Everything went according to plan, and you were confident that the investment would deliver the expected results.

Initially, there were encouraging signs. Some employees logged in, explored the platform and started using it, and you believed that others would soon follow. But a few months later, usage had started to decline. The same five people seemed to account for almost all the activity, while many of the employees you expected to come on board had never logged in at all. Despite a successful implementation, the technology wasn’t being adopted in the way you’d anticipated. So what had gone wrong?

I’ve spent much of my career trying to understand why this happens. Back in 2015, I was leading Learning for the Financial Services division of a large retailer. We had a constant demand for new joiner training, and I became convinced that digital learning could help us deliver training in a more sustainable and scalable way. As I researched what it would take to make online learning successful, I came across a statistic suggesting that only around 10% of people adopted these types of systems. Whether that number was accurate or not, it raised a question that has stayed with me ever since: if organisations were investing so much time, money and effort into technology, why were so few people actually using it?

A few years later, I founded a digital learning consultancy and spent almost a decade helping organisations take their training online. Across different industries, technologies and employee groups, I encountered the same challenge repeatedly. Organisations could successfully implement technology without achieving the sustained adoption needed to realise its value. That experience fundamentally changed how I think about technology implementation.

Why technology adoption is harder than we think

I believe three assumptions frequently undermine our efforts to achieve sustained technology adoption.

1. We assume that if something is beneficial, people will use it.

We tend to approach technology adoption as a rational decision. If a new system makes someone’s work faster, easier or more efficient, surely they’ll choose to use it. But knowing something is beneficial doesn’t automatically translate into behaviour change. Consider exercise. Most of us understand its benefits, yet that knowledge alone doesn’t guarantee that we’ll exercise consistently.

The same is true in the workplace. Employees may recognise that a new technology offers advantages while continuing to rely on familiar tools and processes. Established habits are powerful, and changing them requires more than presenting a compelling reason to do something differently. We need to recognise that the decision to adopt a new technology isn’t always driven by a rational assessment of its benefits.

2. We assume that proving the value will convince people.

When communicating the benefits isn’t enough, organisations often try to demonstrate the value more convincingly. We conduct time studies, calculate efficiencies and produce evidence showing that the new technology is objectively better. Imagine replacing a manual process involving spreadsheets and emails with a system designed to make the work faster and easier. The analysis demonstrates measurable time savings, yet employees continue using their spreadsheets.

The problem isn’t necessarily that the technology lacks value. It’s that objective value and perceived value aren’t always the same thing. A process might be faster according to the data, but if it feels more complicated or unfamiliar to the employee, they may not experience it as an improvement. We can demonstrate the value of a technology, but we can’t assume that employees will perceive that value in the same way. Until they experience the benefits for themselves, the evidence may do little to change their behaviour.

3. We assume the working environment will support the change.

Perhaps the most significant oversight is assuming that once a new technology is launched, the organisation will naturally adapt around it. Consider a dashboard that gives leaders real-time access to information. The organisation invests in the technology, trains employees and encourages its use. Yet leaders continue asking their teams to prepare spreadsheets. Similarly, a new system might be introduced to streamline a process while the old reports, templates and approval processes remain in place.

Employees quickly recognise these contradictions. Although the organisation is communicating one expectation, its everyday practices are reinforcing another. People pay attention to what leaders ask for, what gets measured and what gets rewarded. When those signals contradict the intended change, established behaviours are likely to persist. People don’t simply follow what gets communicated. They follow what gets reinforced.

These three assumptions point to a common problem: organisations often focus on introducing technology without giving equal attention to the conditions required for people to adopt it.

Four conditions for sustained technology adoption

Across the technology implementations I’ve worked on, I’ve found that sustained adoption depends on creating four conditions. People need to understand the value of the technology, know where it fits into their work, experience its benefits and operate in an environment that reinforces continued use. I use four questions to guide this thinking.

1. Why would I use it? — Value

Most organisations communicate the benefits of new technology. The challenge is that we often describe those benefits from the organisation’s perspective rather than the employee’s. We talk about improving efficiency, increasing productivity and streamlining processes. These may be compelling business outcomes, but they don’t necessarily explain why an individual employee should change how they work. Employees are asking a more personal question: What does this mean for me?

Instead of communicating that a system will improve efficiency, explain how it could save someone 15 minutes each time they complete a particular task. Instead of saying it will streamline a process, explain that they will no longer need to enter the same information in three different places. The difference is specificity and relevance. When people understand how technology will improve their own experience of work, they have a more meaningful reason to consider changing their behaviour.

2. Where does it fit? — Application

This is the condition I believe organisations most frequently overlook. Technology training typically focuses on functionality. We teach employees how to navigate systems, find information and complete tasks. But knowing how to use a feature isn’t the same as knowing when to use it. Technology is built around features, while people work in processes. If we want employees to adopt technology, we need to help them understand how it fits into their existing responsibilities and routines.

Consider the implementation of a performance management system. We might train managers to create goals, capture performance notes and complete reviews, but unless we connect those features to their actual responsibilities, managers are left to work out how the system fits into their existing routines. Should they use it when preparing for a one-on-one, setting goals with an employee, reviewing progress or preparing for a performance conversation? Which existing activities should the system replace, and how should their way of working change?

These questions matter because employees don’t automatically translate system functionality into new work practices. Effective adoption requires us to connect technology to the processes, decisions and activities that make up someone’s working day. Otherwise, we risk creating employees who are competent users of a system but rarely find a reason to use it.

3. Have I experienced the benefit? — Experience

There’s an important difference between understanding the promised benefits of a technology and experiencing those benefits personally. Communication can establish expectations, and demonstrations can show what’s possible, but neither guarantees that employees will believe the technology is genuinely better than their existing way of working. That belief develops through experience. When someone completes a task more easily, saves time or solves a problem faster, the value becomes tangible.

This means our approach to learning and change should extend beyond explaining benefits and teaching functionality. We need to create opportunities for people to experience the benefits through meaningful work. Rather than simply demonstrating how a system operates, help employees use it to complete an actual task or solve a problem they encounter regularly. The objective is to create moments when employees recognise for themselves that the new way of working offers an advantage. Communication creates expectation. Experience creates belief.

4. What will make this stick? — Habit

Initial adoption is one challenge; sustaining it is another. Organisations sometimes turn to incentives, competitions and leaderboards to encourage technology usage. These activities may increase engagement temporarily, but they don’t necessarily establish lasting habits. Sustained adoption requires an environment that consistently reinforces the desired behaviour. In my experience, three elements are particularly important.

Leadership plays a critical role in reinforcing new behaviours. Leaders need to demonstrate the behaviours they expect from others. If they’ve introduced a new reporting dashboard, they should use it to access information rather than continuing to request spreadsheets from their teams. Their actions signal whether the new technology is genuinely part of how the organisation expects people to work.

The working environment must also support the change. Organisational processes, measures and routines need to reinforce the new way of working. If old templates, reports and approval processes remain in place, employees may have little reason to abandon them. The systems and processes surrounding the technology need to evolve alongside it.

The technology itself needs to be accessible, intuitive and genuinely useful. Even strong leadership and effective change management cannot compensate indefinitely for technology that makes people’s work unnecessarily difficult. When these three elements reinforce one another, the technology gradually becomes embedded in everyday work. Employees no longer think of it as a new system they’re expected to use; it simply becomes how work gets done.

It’s time to rethink what successful implementation means

For L&D and HR professionals, achieving sustained technology adoption requires us to think differently about our role in implementation. Delivering training, communicating benefits and supporting a successful launch are important contributions, but they are not the outcomes we’re ultimately trying to achieve. Our responsibility extends to helping the organisation realise the value of its investment by ensuring that technology becomes embedded in how people work.

The four questions I’ve outlined provide a practical way to approach this challenge. We need to ensure that employees understand why they should use the technology, know where it fits into their work, have opportunities to experience its benefits and operate in an environment that reinforces continued use. These conditions are relevant whether we’re implementing a learning platform, a performance management system, an HR solution or new AI technology.

I also believe we need to reconsider how we measure implementation success. Launching on schedule, delivering training and achieving initial login targets may tell us whether we’ve implemented the technology effectively, but they tell us very little about whether we’ve achieved meaningful adoption. To understand whether our investment is delivering value, we need to look beyond initial activity and examine whether the technology is changing how work gets done.

Next time you introduce a new piece of technology, my challenge to you is this: instead of asking whether the implementation was successful, check whether people are working differently six months later. That will tell you if you’ve been successful or not.

About the author

I’m Sheree Comninellis. I work in corporate Learning & Development, with a focus on building enterprise capability. I write and speak about learning, leadership and change.

Have a perspective to share or want to continue the conversation? Connect with me on LinkedIn or get in touch through my website.

The New Economics of Learning: Making Smarter Build vs Buy Decisions in an AI World

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For years, one of the biggest considerations in deciding whether to build or buy learning solutions has been the time and cost involved. Building something ourselves takes time, requires specialist skills and can be expensive. Buying an existing solution is often quicker and easier. But what happens when AI fundamentally changes that equation? Learning teams can now develop outlines, draft content, create activities, adapt materials and produce digital learning experiences in a fraction of the time these tasks previously required.

While this doesn’t mean creating learning is free, or that AI-generated content is automatically good, it does mean that some of our traditional reasons for outsourcing or purchasing learning solutions deserve closer examination.

Every L&D team faces decisions about whether to develop learning internally, curate existing content, customise a solution or partner with an external provider.

But before we ask what we can afford to buy or whether we have time to build, we should be asking what capability the organisation needs, who needs it and what kind of learning experience will genuinely address that need.

When I’m faced with a build, buy or partner decision, I work through six key questions. In this article, I’ll share those questions and demonstrate how I apply them in practice.

Six questions to guide your build vs curate decisions

1. Strategic importance: How critical is this capability?

Not all learning needs carry the same strategic weight. Some capabilities are fundamental to an organisation’s competitive advantage, culture or ability to deliver its strategy. Others are more generic skills that employees need to perform effectively.

When a capability is strategically important, there may be a stronger case for investing in a solution that is tailored to the organisation.

That doesn’t automatically mean building it internally. But it does mean we should be particularly deliberate about how the capability is developed.

2. Scale and access: How many people need it?

Scale changes the economics of learning. A programme needed by ten employees may warrant a very different investment approach from one needed by thousands.

We also need to think about access. Is this something a small group of specialists requires, or a capability that should be available to everyone?

However, scale alone shouldn’t determine the decision. A large audience can still be well served by curated content.

3. Contextual relevance: How specific does the learning need to be?

This is one of the most important considerations for me.

How much does the learning need to reflect our organisation, industry, culture, region or audience?

4. Quality: Does the solution meet the standard we need?

The availability of content doesn’t necessarily mean it’s good content. We need to evaluate the quality of the learning experience, not simply the accuracy or appearance of the material.

Does it create opportunities for practice? Is it engaging and accessible? Will it help learners apply what they’ve learned?

This is particularly important in an AI-enabled world, where producing polished-looking content has become remarkably easy.

A professional-looking programme is not necessarily an effective learning intervention.

5. Longevity: How long will the content remain useful?

Some learning content has a long shelf life. Other content needs frequent updates as products, processes, regulations or technology change. This influences the economics of our decision.

If we expect to use a programme repeatedly over several years, investing in an internal asset may make sense.

If the content changes frequently, we need to consider how easily it can be maintained and who will be responsible for keeping it current.

6. Market availability: What’s already out there?

Finally, we need to understand what the market can offer.

Sometimes an excellent solution already exists, and building something similar would be an unnecessary use of our resources.

Sometimes existing content is good but needs customisation.

And sometimes the market doesn’t offer anything sufficiently relevant, making a bespoke solution the stronger option.

The question isn’t whether we can build something. It’s whether building it is the most sensible strategic choice.

From framework to decision

I deliberately describe these six considerations as a decision matrix rather than a formula. I don’t believe we should count how many boxes we’ve ticked and declare that building or buying has won. The factors need to be weighed together.

A highly specialised capability, with limited market availability and a long useful life, may justify an internal build.

A generic capability needed by thousands of employees may be better served through curated content.

Strong existing content that lacks cultural or organisational relevance may be best addressed through curation and customisation.

And sometimes the right decision is to partner with a specialist provider.

The objective isn’t to build everything ourselves. It’s to make deliberate decisions about where different approaches will create the most value.

Of course, we can’t ignore cost. Once we’ve identified the strategically suitable options, then we need to evaluate their economics.

But we shouldn’t allow the price tag we see to determine our learning strategy.

Applying the framework: Building AI literacy

Let me share an example of how I’ve applied these questions in practice.

At a leading retailer, we identified AI literacy as an important capability for our employees. In my division alone, we had approximately 500 people who could benefit from developing these skills.

Rather than immediately selecting a learning solution, I considered the six questions.

Strategic importance: AI literacy was becoming increasingly important to how our people would work. This wasn’t a specialist capability for a select few.

Scale and access: With approximately 500 employees in my division, I wanted a solution that could be made available to everyone, rather than limiting access to a small group.

Contextual relevance: Much of the available AI learning content was generic. I wanted something that connected the concepts to our industry, business environment and employees’ roles.

Quality: The programme needed to provide a meaningful introduction to AI and help employees understand how they could apply what they learned in their work.

Longevity: AI was evolving rapidly, so I needed to consider how we could keep the learning relevant as the technology developed.

Market availability: There were already good programmes available. I didn’t believe we needed to create everything from scratch, but I did believe we needed something tailored to our audience.

With these considerations in mind, I evaluated three possible approaches.

Build? Not necessarily. AI literacy was a relatively broad capability, and suitable content already existed in the market. Developing everything from scratch would have meant investing time and resources in recreating material that was already available.

Buy? Yes, but not simply purchasing an off-the-shelf programme. The content needed to be relevant to our employees and accessible at scale. I needed a solution we could adapt to our context and make available through our own learning platform.

Partner? Bringing in an external provider to facilitate training sessions was another possibility. However, this would make reaching approximately 500 employees more challenging, particularly if we wanted everyone to have access to the learning.

The most appropriate approach was therefore to buy and customise.

I investigated the cost of sourcing an existing programme, adapting it to our needs and hosting it internally. The solution I selected involved a once-off investment, including customisation and perpetual usage rights.

Approximately 500 employees could access a programme relevant to their work.

The decision wasn’t simply about choosing the cheapest option. It was about selecting the approach that best addressed our capability need, and then ensuring that the economics made sense.

The investment L&D teams should be making

AI presents an incredible opportunity for L&D teams. I believe that every L&D team, regardless of size, should be developing the capability to create at least some of its own digital learning solutions.

I see three areas of investment.

Learning design strategy. The ability to diagnose capability needs, understand business priorities and select the most appropriate learning response.

Instructional design. The ability to create effective learning experiences that support practice, application and transfer, rather than simply producing attractive content.

Technical capability. The ability to use AI and digital learning tools effectively, critically and responsibly.

When these capabilities come together, L&D teams have more choices. They can respond more quickly, create contextually relevant learning experiences and reduce unnecessary dependence on external development.

This doesn’t eliminate the need for providers. In fact, I believe specialist providers will remain incredibly valuable, particularly when they bring expertise, experiences or outcomes that internal teams cannot reasonably replicate.

But the value proposition is changing. As internal teams become more capable, generic content development becomes harder to justify at a premium. The strongest providers will be those who offer distinctive expertise and genuine value beyond what organisations can create themselves.

Making the next decision differently

The next time a learning request lands on your desk, resist the temptation to immediately search for a course or open your authoring tool.

Start with the capability the organisation needs.

Consider its strategic importance, the scale of the need, contextual relevance, quality, longevity and what’s available in the market.

Then evaluate the economics of the most suitable approaches, including how AI might change the cost and effort of creating or adapting a solution.

And if your team doesn’t currently have the skills to execute the option you believe is best, consider whether that’s a capability worth developing.

Because when you do this, the investment isn’t only in the next programme. It’s in your team’s ability to respond to the many learning needs that will follow.

Not building should be a strategic choice, not a capability limitation.

AI hasn’t changed the need for sound learning strategy. It has changed what skilled L&D professionals can do with the time and resources available to them. And that gives us an opportunity to make better decisions about where we invest, what we create and when we partner.

About the author

I’m Sheree Comninellis. I work in corporate Learning & Development, with a focus on building enterprise capability. I write and speak about learning, leadership and change.

Have a perspective to share or want to continue the conversation? Connect with me on LinkedIn or get in touch through my website.